/*
* JENES
* A time and memory efficient Java library for genetic algorithms and more
* Copyright (C) 2011 Intelligentia srl
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
package jenes.tutorials.problem1;
import jenes.Fitness;
import jenes.GeneticAlgorithm;
import jenes.chromosome.BooleanChromosome;
import jenes.population.Individual;
import jenes.population.Population;
import jenes.population.Population.Statistics.Group;
import jenes.stage.AbstractStage;
import jenes.stage.operator.common.OnePointCrossover;
import jenes.stage.operator.common.SimpleMutator;
import jenes.stage.operator.common.TournamentSelector;
import jenes.tutorials.utils.Utils;
/**
* Tutorial implementing a basic genetic algorithm.
* The problem consists in finding a vector full of zeros or ones.
*
* @author Luigi Troiano
*
* @version 2.0
*
* @since 1.0
*/
public class BooleanProblem {
private static int POPULATION_SIZE=50;
private static int CHROMOSOME_LENGTH=100;
private static int GENERATION_LIMIT=1000;
public static void main(String[] args) throws Exception {
Utils.printHeader();
System.out.println();
System.out.println("TUTORIAL 1:");
System.out.println("This algorithm aims to find a vector of booleans that is entirely true or false.");
System.out.println();
// Random.getInstance().setStandardSeed();
Individual<BooleanChromosome> sample = new Individual<BooleanChromosome>(new BooleanChromosome(CHROMOSOME_LENGTH));
Population<BooleanChromosome> pop = new Population<BooleanChromosome>(sample, POPULATION_SIZE);
Fitness<BooleanChromosome> fit = new Fitness<BooleanChromosome>(false) {
@Override
public void evaluate(Individual<BooleanChromosome> individual) {
BooleanChromosome chrom = individual.getChromosome();
int count = 0;
int length=chrom.length();
for(int i=0;i<length;i++)
if(chrom.getValue(i))
count++;
individual.setScore(count);
}
};
GeneticAlgorithm<BooleanChromosome> ga = new GeneticAlgorithm<BooleanChromosome>
(fit, pop, GENERATION_LIMIT);
AbstractStage<BooleanChromosome> selection = new TournamentSelector<BooleanChromosome>(3);
AbstractStage<BooleanChromosome> crossover = new OnePointCrossover<BooleanChromosome>(0.8);
AbstractStage<BooleanChromosome> mutation = new SimpleMutator<BooleanChromosome>(0.02);
ga.addStage(selection);
ga.addStage(crossover);
ga.addStage(mutation);
ga.setElitism(1);
ga.evolve();
Population.Statistics stats = ga.getCurrentPopulation().getStatistics();
GeneticAlgorithm.Statistics algostats = ga.getStatistics();
System.out.println("Objective: " + (fit.getBiggerIsBetter()[0] ? "Max! (All true)" : "Min! (None true)"));
System.out.println();
Group legals = stats.getGroup(Population.LEGALS);
Individual solution = legals.get(0);
System.out.println("Solution: ");
System.out.println( solution );
System.out.format("found in %d ms.\n", algostats.getExecutionTime() );
System.out.println();
Utils.printStatistics(stats);
}
}
|
|
|
